The recently proposed neural additive models (NAM) [1]} are state-of-the-art methods of interpretable machine learning.
Since NAM learns a linear combination of neural networks corresponding to each input feature, it can model nonlinear relationships between each input feature and the output.
In addition, NAM can provide straightforward interpretations and has a predictive performance comparable to that of multi-layer perceptron (MLP) and tree-based methods, such as XGBoost [2]}.
However, NAM can only model 1st-order feature interactions.
Therefore, NAM cannot capture the co-relationships between input features even though there are many such relationships in real-world data.
More recently, [3]} have proposed NodeGAM and NodeGA\(^{\text{2}}\) M which are neural decision tree-based generalized additive models (GAMs).
However, NodeGAM and NodeGA\(^{\text{2}}\) M also merely can learn 1st-order and 2nd-order feature interactions, respectively.
If there is an interpretable machine learning method that can model high-order feature interactions, it will not only provide high-quality interpretations, but also show better predictive performance.
